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Abstract

Social computing has led to new approaches for Knowledge Translation (KT) by overcoming the temporal and geographical barriers experienced in face-to-face KT settings. Social computing based discussion forums allow the formulation of communities of practice whereby a group of professionals disseminate their knowledge and experiences through online discussions on specialized topics. In order to successfully build an online community of practice, it is important to improve the connectivity between like-minded community members and between like-topic discussions. In this paper we present a Medical Online Discussion Analysis and Linkages (MODAL) method to identify affinities between members of an online social community by applying: (a) social network analysis to understand their social communication patterns during KT; and (b) semantic content analysis to establish affinities between different discussions and professionals based on their communicated content. Our approach is to establish linkages between users and discussions at the semantic and contextual levels—i.e. we do not just link discussions that share exact medical terms, rather we link practitioners and discussions that share semantically and contextually similar medical terms, thus accounting for vocabulary variations, concept hierarchies and specialized clinical scenarios. MODAL incorporates two novel semantic similarity methods to analyze online discussions using: (i) the Generalized Vector Space Model (GVSM) that leverages semantic and contextual similarity to find similarities between discussion threads and between practitioners; and (ii) an extension of the Balanced Genealogy Model (BGM) so that we are able to address non-leaf mappings, issues of homonymity noted in medical terminologies, and further contextualization of the similarity measures using information content measures. We have implemented a similarity metric that captures the concept of "interest" between users or threads, i.e., a numeric measure of how interested user A is in user B, or how much of the information contained in thread A is related to thread B. MODAL measures the "interest" one professional has in another professional within the online community, and then uses this metric to identify those professionals that are sought by other professionals for expert advice—the content experts. Furthermore, by incorporating the interest measures with SNA, MODAL is able to identify the content experts within the community, and analyze the content of their conversations to determine their areas of expertise. Given the short and unstructured nature of online communications, we use the MeSH medical lexicons and the medical text analysis tools, i.e. Metamap, to map the unstructured narrative of online discussions to formal medical keywords based on the MeSH lexicon. MODAL is tested on two online professional communities of healthcare practitioners: (a) Pediatric Pain Mailing List is a community of 460 clinicians from around the world--over a four year period 2505 messages were shared on 783 different discussion threads; (b) SURGINET is a community of 865 clinicians from around the world that use the forum to discuss general surgical issues-it contains over 17000 messages on 2111 threads by 231 users. MODAL is able to identify content experts and link like-minded practitioners based on the content of their conversations rather than on direct ties between them.

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/content/papers/10.5339/qfarc.2014.ITOP0663
2014-11-18
2020-08-12
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2014.ITOP0663
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